A personalized recommender system presents information of items, e.g. movies, books, music, news, pictures, that are likely to be of interest to a user, i.e. the system recommends suitable items to the user after logon. A conventional recommender system may use content-based information filtering, involving a mapping of item content on a user profile, or collaborative information filtering, involving a prediction of a user's rating of an item, using a database of user ratings. More specifically, a user-based collaborative information filtering recommender system comprises finding other users that have a similar rating pattern as the first user, and predicting the first user's rating of not-consumed items, based on said other users rating of said items.
A user rating can be expressed explicitly by a user on a numerical scale, or by a ranking of items, or expressed implicitly, e.g. by a purchase, or by the time spent viewing an item.
In a conventional user-based collaborative filtering recommender system, the ratings from a first user are matched against the ratings from other users, in order to find other users that have similar rating pattern as the first user. Thereafter, the items that said other users have given high ratings, and that are not yet consumed by the first user, will be recommended to the first user.
The similarity of the rating patterns are typically determined by comparing the rating correlation of the co-rated items, the correlation calculated e.g. as the Pearson correlation or the adjusted cosine correlation.
Conventionally, a user rating of an item in a recommender system is static, i.e. the item will keep its initial rating, and will remain recommendable independently of the age of the rating and of the age of the item itself. This may be appropriate e.g. in systems recommending movies and books. However, in a system recommending news, the news items are normally only valid for a very short period of time, which is commonly referred to as “item churn”. Thus, the age of a news item should preferably influence the validity of its user rating.
Das et. al: “Google News Personalization: Scalable Online Collaborative Filtering”, WWW 2007/Track: Industrial Practice and Experience, May 8-12, 2007 describes handling of item churn behaviour by an updating and retrieval of the “click” recording and statistics in real time. According to the handling of item churn described in this article, every click made by a user influences the rating of the different news stories, and may change the set of recommended news story items. Thereby, the high item churn associated with news stories can be handled, enabling a recommendation of relevant news stories to users shortly after they appear in various source, during a suitable “expiry window”. However, the article does not address how to calculate this “expiry window”.
Ding and Li: “Time weight collaborative filtering”, 14th ACM international conference on information and knowledge management, pages 485-492, ACM Press, New York, 2005, describes a time function, which is used to weight down the similarity between two items, if the ratings from the active user are old. However, this time function is only applicable on item-based collaborative filtering, since it can only weight down item similarities, and not user similarities, i.e. the similarity of the rating pattern of co-rated items from two different users. Another drawback occurs if two items have new ratings from most user's, but the ratings from the active user are comparatively older, in which case the similarity between the two items will be weighted down due to the old ratings from the active use, regardless of the ratings from the other users.
Gordea and Zanker: “Time filtering for Better Recommendation with Small and Sparse Rating Matrics”, LNCS 4831, pages 171-183, 2007, describes a time decay filter and a time window filter, enabling a recommendation of new items, or items with a periodic interest, e.g. to recommend ice-skating only during the winter season. This solution weights down a final rating score based on the age of the actual item, not based on the age of the ratings of the item. Thus, if an item itself is old, it will be weighted down, even if the item has high ratings, and the ratings are recently added.
Thus, the above-described conventional recommender systems have several drawbacks, and it still presents a problem to achieve an improved recommender system, in which the age of the ratings is allowed to influence the recommendation of items, and which is applicable to e.g. a user-based collaborative filtering recommender system.